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<p>The use of the oppositional crayfish optimization algorithm (OCOA) for the solution of electric vehicle charging station (EVCS) incorporation and network reconfiguration (NR) with distributed generation (DG) and capacitors placement problems in a radial distribution network (RDN), where active power loss and annual energy loss cost minimization are the main objectives of the study. An improved version of the crayfish optimization algorithm (COA) is created by adding oppositional behaviour into the primary COA algorithm for the generation of an opposite primary population to find the optimal solution. Two test networks (33-bus and 69-bus) are used to examine the effectiveness of the proposed OCOA algorithm with three different scenarios and they are (i) EVCS with unity power factor (UPF) DG and capacitor placement, (ii) EVCS with optimal power factor (OPF) DG and capacitor placement, and (iii) EVCS inclusion and network reconfiguration with optimal power factor (OPF) DG and capacitor placement. The OCOA method allows improvements of 81.45%, 82.98%, and 87.24% for the 33-bus and 95.81%, 96.52%, and 96.72% for the 69-bus in active power loss value for three cases, respectively. Also, for AELC minimization, the OCOA allows improvements of 71.41%, 74.58%, and 76.75% for the 33-bus and 84.82%, 88.09%, and 89.15% for the 69-bus, respectively.</p>

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Oppositional crayfish optimization algorithm for DG and capacitors allocation in reconfigured radial distribution networks with the impact of electric vehicle charging station

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Indrajit Dey,
  • Provas Kumar Roy,
  • Falguni Chakraborty

摘要

The use of the oppositional crayfish optimization algorithm (OCOA) for the solution of electric vehicle charging station (EVCS) incorporation and network reconfiguration (NR) with distributed generation (DG) and capacitors placement problems in a radial distribution network (RDN), where active power loss and annual energy loss cost minimization are the main objectives of the study. An improved version of the crayfish optimization algorithm (COA) is created by adding oppositional behaviour into the primary COA algorithm for the generation of an opposite primary population to find the optimal solution. Two test networks (33-bus and 69-bus) are used to examine the effectiveness of the proposed OCOA algorithm with three different scenarios and they are (i) EVCS with unity power factor (UPF) DG and capacitor placement, (ii) EVCS with optimal power factor (OPF) DG and capacitor placement, and (iii) EVCS inclusion and network reconfiguration with optimal power factor (OPF) DG and capacitor placement. The OCOA method allows improvements of 81.45%, 82.98%, and 87.24% for the 33-bus and 95.81%, 96.52%, and 96.72% for the 69-bus in active power loss value for three cases, respectively. Also, for AELC minimization, the OCOA allows improvements of 71.41%, 74.58%, and 76.75% for the 33-bus and 84.82%, 88.09%, and 89.15% for the 69-bus, respectively.